Pose the question plainly: how do you build a good model when the data lives in ten hospitals that legally can't pool it? Federated learning answers the training half, train locally, share only model updates. This 2021 publication answers the design half, search for the architecture itself, federated.
US20210374502A1 (published December 2, 2021) couples neural architecture search, the propose-evaluate loop we've covered, with a federated setup, tagged G06N 3/0454, G06N 3/08, and imaging classes. The inventors' other work clusters in medical imaging, the canonical setting where data is sensitive and siloed.
Under the hood there are now two distributed loops. The architecture search proposes a candidate design. That design is trained federally, each site computes updates on its own data and sends back only the gradients. The aggregated result scores the candidate, which steers the next proposal. Data never leaves; only math does.
Why this matters for the sector: privacy-preserving training is one of the few credible answers to the 'where does the data come from' question that haunts every AI deployment in regulated industries. A patent that federates not just training but model design shows how seriously that constraint was being engineered around by 2021.
The caveat in the house style: federated methods trade communication overhead and convergence headaches for privacy, and a publication describes a method, not a deployment. What it establishes is that by late 2021, design the model where you can't centralize the data had moved from research aspiration to filed IP.